impact = 1
p_opt = 0
while X_test.empty==False:
	while impact < 25:
		for s in samp:
			instance = X_test[:1]
			instance['samp'] = s
			dt_pred = dt.predict(instance);
			print(" for sampling rate is " + str(s) + "% predicted loss is: " + str(dt_pred))
			if dt_pred[0] < impact:
				if s > p_opt:
					p_opt = s
			else:
				print("for traffic of " + str(int(instance['vm'])) + " vms:"  + " and " + str(int(instance['bps_expected'])) +" Mbps " +"use sampling rate of " + str(p_opt) + "%")
		list_opt=[int(instance['vm'].get_values()),int(instance['bps_expected'].get_values()),p_opt]
		full_opt = np.vstack([full_opt,list_opt])
		full_opt = pd.DataFrame(full_opt, columns =['vm','bps','p_opt'])
		full_opt.to_csv('true_p_impact'+str(impact))
		impact +=1
		p_opt = 0
		print
	X_test = X_test.drop(X_test.loc[(X_test['vm'] == int(instance['vm'].get_values())) & (X_test['bps_expected'] == int(instance['bps_expected'].get_values()))].index)